Faceted classification is based on the principle that information has a multi-dimensional quality, and can be classified in many different ways. Subjects of an informational domain are subdivided into facets (or more simply, categories) to represent this dimensionality. The attributes of the domain are related in facet hierarchies. The materials within the domain are then identified and classified based on these attributes.
FIG. 1 illustrates the general approach of faceted classification in the prior art, as it applies (for example) to the classification of wine.
Faceted classification is known as an analytico-synthetic method, as it involves processes of both analysis and synthesis. To devise a scheme for faceted classification, information domains are analyzed to determine their basic facets. The classification must then be synthesized (or built) by applying the attributes of these facets to the domain based on constructive rules.
Faceted classification is a very labor-intensive and intellectually challenging endeavor. In facet analysis, structural patterns (such as semantic or syntactical structures) must be identified within the domain. There are many different patterns that may identify facets and attributes within a domain. While people can be trained to identify these patterns on small (local) data sets, the task becomes prohibitively difficult as the size of the domain increases.
To help address the complexity of the task, scholars have devised rules and guidelines for faceted classification. Though technology has been enlisted in the service of facet analysis, by and large, this technology has been applied within the historical methods and organizing principles of traditional facet analysis theory. People remain key inputs and facet analysis remains an overwhelming human activity.
Thus, there are many disadvantages with the current state of the art in automated facet analysis. The input of human cognition is requisite, as there are no universal patterns or heuristics for facet analysis that work across all information domains. Presently, only humans possess the fall breadth of pattern recognition skills.
Hybrid systems that involve humans at critical stages in the process, typically early on in the process, are often bottlenecked in their classification efforts. As such, the process remains slow and costly. Systems are needed that accept classification data from people in a more decentralized, ad hoc manner that does not require centralized control and authority.
Humans are adept at assessing the relationships between informational elements at a small scale, but fail to manage the complexity over an entire domain in the aggregate. Systems are needed that are able to aggregate small, localized human inputs across an entire domain of information.
Hybrid systems that are based on existing universal schemes of faceted classification rarely apply to the massive and rapidly evolving modern world of information. There is a pressing need for custom-designed schemes, specialized to the needs of individual domains.
Since universal schemes cannot be applied universally, there is also a need to connect different domains of information together. Systems of facet analysis are needed to provide for universal facets and attributes that may be combined in novel ways to generate custom-designed classification schemes. In other words, facet analysis may provide a means for fundamentally connecting disparate domains together, without prescribing the use of universal classifications.